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Switch cabinet fault classification method based on semi-supervised learning

A semi-supervised learning and fault classification technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., it can solve the problems of high dimension of monitoring features, difficulty in fault monitoring of each module of the switch cabinet, and increased difficulty in fault classification.

Pending Publication Date: 2020-09-04
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, relying on traditional monitoring methods to monitor the faults of each module of the switchgear is a very difficult task, so how to use the data of the switchgear to realize fault classification is an important research direction at present.
In addition, the monitoring feature quantity dimension of the switch cabinet sample data is relatively high, and most of them are unlabeled, which increases the difficulty of fault classification

Method used

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  • Switch cabinet fault classification method based on semi-supervised learning
  • Switch cabinet fault classification method based on semi-supervised learning
  • Switch cabinet fault classification method based on semi-supervised learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] figure 1 It is a flow chart of the switchgear fault classification method based on semi-supervised learning in Embodiment 1 of the present invention.

[0058] Such as figure 1 As shown, the switchgear fault classification method based on semi-supervised learning in this embodiment is used to classify the fault types of the switchgear, including the following steps:

[0059] Step 1. Obtain sample data of the fault type of the switchgear, which includes labeled sample data with known fault types and unlabeled sample data with unknown fault types.

[0060] Table 1 Monitoring characteristic quantity of switchgear

[0061]

[0062] In Table 1: FS1 and FS2 reflect the working environment of the busbar; FS3-FS7 are the characteristic quantities of electrical parameters, reflecting the internal and external system faults; FS8-FS9 reflect the partial discharge of the switchgear; FS11 reflects the temperature change caused by partial discharge, etc.; FS12 reflects The break...

Embodiment 2

[0125] This embodiment selects the sample data of the fault type of a known switchgear in a power grid as a data sample, randomly selects a part as labeled samples, and the rest of the samples as unlabeled samples, and adopts the switchgear fault based on semi-supervised learning in Embodiment 1 The classification method is used to classify the fault types of the switchgear. The specific classification process is as follows:

[0126] First, preprocess the sample data:

[0127]

[0128] Accordingly, a labeled sample data set X of the switchgear is established 1 ={x 1 ,x 2 ,...,x m} and unlabeled sample dataset X 2 ={x m ,x m+1 ,...,x n}.

[0129] For the labeled sample data, the Laplacian score method is used for feature selection, and the training set S is obtained. 1 ={s 1 ,s 2 ,...,s m}. Laplace score formula:

[0130]

[0131] Train an initial classifier of fault types:

[0132]

[0133] Determine the sample point position of the switchgear accordin...

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Abstract

The invention provides a switch cabinet fault classification method based on semi-supervised learning, and the method comprises the following steps: 1, obtaining the sample data of a fault type of a switch cabinet, wherein the sample data comprises the label sample data with the known fault type and the label-free sample data with the unknown fault type; 2, performing feature selection on the labeled sample data by adopting a Laplace score method to obtain a training set; 3, training the training set by adopting an S3VDD algorithm to obtain an initial classifier of the fault type; 4, calculating the membership degree of the label-free sample data to the initial classifier through a sample labeling method, and expanding the training set through the membership degree to obtain an expanded training set; 5, adopting an S3VDD algorithm to train the extended training set until the membership degrees of all the label-free sample data are consistent, ending the algorithm, and obtaining a trained classifier; and step 6, classifying the fault types by using the trained classifier.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of electrical equipment, and in particular relates to a method for classifying switch cabinet faults based on semi-supervised learning. Background technique [0002] Switchgear is a very important electrical equipment in the power system. With the development of my country's economy, the modern power system has higher and higher requirements for power quality. However, due to various reasons such as human misoperation and bad weather, the number of accidents caused by the deterioration of the operating state of the switchgear is still high. [0003] In order to prevent the failure of the switchgear, in addition to real-time monitoring of the operating status of the system and the use of relevant data to judge the operating status of the switchgear, it is also necessary to be able to quickly diagnose when the switchgear fails, and determine the cause and type of the failure. However, relying on tradi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/213G06F18/2155G06F18/24
Inventor 杨帆黄河李东东赵耀林顺富
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER